Written Assignment

Candidate name: Juan Pablo Herrera

Question 2 - Crime Data

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
import plotly.express as px

%matplotlib inline
import plotly 
plotly.offline.init_notebook_mode()

Read Data

# Read and create date column
crime_df = (pd.read_csv("csv/5_crime_data_REQ100529.csv")
            .assign(date = lambda x: x.apply(lambda row: datetime.date(row.YEAR, row.MONTH, row.DAY), axis = 1))
)

Analyze the data by creating at least two visualizations.

Time Series to analize behaviours over time:

1. We notice that there was a big crime spike in June 2011 that involved mischief and breaking and entering.
2. We notice that there was a big crime spike in Oct 2010 that involved mischief and theft from vehicle.
3. We notice a seasonal trend in bike theft during summer months.
4. Break and entering had a big growth in July 2012.
5. We can notice an upward trend in theft from vehicle since July 2012.

Map to notice troublesome areas.

to_plot = crime_df.assign(year_month = lambda x: x.YEAR.astype(str).str[0:4] + '-' + x.MONTH.astype(str).str.zfill(2)).groupby(['year_month', 'TYPE'], as_index=False).size()
sns.lineplot(data = to_plot, x = 'year_month', y = 'size', hue = 'TYPE')
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0)
plt.xticks(rotation=90)
plt.title('Time series of crime types')
plt.show()

map_df = crime_df.groupby('NEIGHBOURHOOD', as_index = False).agg({'Latitude': 'mean','Longitude': 'mean', 'date':'count'})

#drawing figure
fig = px.scatter_mapbox(map_df,
        lat='Latitude',
        lon='Longitude',
        color='date',
        opacity=0.5,
        center=dict(lon=df['Longitude'].mean(),
                    lat=df['Latitude'].mean()),
        zoom=11,
        size=df.date * 500)

fig.update_layout(mapbox_style='carto-positron', width = 1200, height = 900)

fig.show()

Analyze the data by creating at least two visualizations.

1. Which date/dates (year, month, day) has/have the highest crime rate in Vancouver?
2. Which date/dates (year, month, day) has/have the lowest crime rate in Vancouver?

Determine the number of crimes per crime type for the above days.

# Get crime counts per day
crime_counts = (crime_df
        .groupby('date', as_index = False)
        .agg(crime_count = ('TYPE', 'count'))
        .sort_values('crime_count',ascending = False)
)


print(f'Day with more crime counts: {crime_counts.head(1).date.item()} with {crime_counts.head(1).crime_count.item()} crimes')
display()
print('Crime type distribution:')
top_crime_days = crime_counts.head(1).date
display(crime_df.loc[(crime_df.date.isin(top_crime_days))].groupby(['date', 'TYPE']).size().sort_values(ascending = False))

print(' ')
print(' ')
print(f'Day with less crime counts: {crime_counts.tail(1).date.item()} with {crime_counts.tail(1).crime_count.item()} crimes')
display()
print('Crime type distribution:')
less_crime_days = crime_counts.tail(1).date
display(crime_df.loc[(crime_df.date.isin(less_crime_days))].groupby(['date', 'TYPE']).size().sort_values(ascending = False))

Day with more crime counts: 2011-06-15 with 649 crimes
Crime type distribution:

date        TYPE                                                
2011-06-15  Mischief                                                367
            Break and Enter Commercial                              174
            Offence Against a Person                                 35
            Theft from Vehicle                                       31
            Theft of Bicycle                                         13
            Other Theft                                              11
            Break and Enter Residential/Other                        10
            Theft of Vehicle                                          6
            Vehicle Collision or Pedestrian Struck (with Injury)      2
dtype: int64

 
 
Day with less crime counts: 2010-11-24 with 36 crimes
Crime type distribution:

date        TYPE                             
2010-11-24  Theft from Vehicle                   10
            Offence Against a Person              6
            Mischief                              4
            Other Theft                           4
            Theft of Vehicle                      4
            Break and Enter Commercial            3
            Break and Enter Residential/Other     3
            Homicide                              1
            Theft of Bicycle                      1
dtype: int64